CN116305783A

CN116305783A - Distributed robust optimal scheduling method, device, equipment and medium for hydropower station

Info

Publication number: CN116305783A
Application number: CN202310045118.5A
Authority: CN
Inventors: 张玄; 卢先锋; 寨战争; 纪经涛; 陈晓鹏; 张焕燊; 李柯睿; 李昊林; 刘雨晴
Original assignee: Guangdong Power Grid Co Ltd; Huizhou Power Supply Bureau of Guangdong Power Grid Co Ltd
Current assignee: Guangdong Power Grid Co Ltd; Huizhou Power Supply Bureau of Guangdong Power Grid Co Ltd
Priority date: 2023-01-30
Filing date: 2023-01-30
Publication date: 2023-06-23

Abstract

The invention discloses a distributed robust optimal scheduling method, device, equipment and medium for a hydropower station, and relates to the technical field of hydropower station scheduling. Comprising the following steps: determining a warehouse-in flow error fuzzy set according to the historical warehouse-in flow estimated value and the historical warehouse-in flow actual value of at least one hydropower station; constructing a hydropower station distribution robust optimization scheduling model according to the historical output, the historical water head and the historical power generation flow of at least one hydropower station and the warehouse-in flow error fuzzy set; reconstructing the hydropower station distribution robust optimization scheduling model based on the conditional risk value theory to obtain a reconstruction optimization scheduling model; and determining the target output, the target water head and the target power generation flow of each hydropower station according to the reconstruction optimization scheduling model and the target flow estimated value of at least one hydropower station. According to the scheme, the storage flow error fuzzy set is determined, the distribution robust optimal scheduling model is established, the waste water flow of the hydropower station is reduced, and the generating capacity and the running stability of the hydropower station are improved.

Description

Distributed robust optimal scheduling method, device, equipment and medium for hydropower station

Technical Field

The invention relates to the technical field of hydropower station scheduling, in particular to a distributed robust optimal scheduling method, device, equipment and medium for hydropower stations.

Background

Hydropower as renewable energy plays an important role in optimizing resource allocation of a power grid, and optimizing and scheduling hydropower stations is also an important subject in the field of power system scheduling. The incoming water flow of the hydropower station presents uncertainty, so that the power generation capacity of the hydropower station is easily affected by the change of the incoming water flow. At present, a random optimization method and a robust optimization method are generally adopted for dispatching optimization of the hydropower station, and the defects that the hydropower station is conservative in dispatching of the reservoir capacity, the waste water flow is large, the generated energy of the hydropower station is low, and the operation risk exists are overcome.

Disclosure of Invention

The invention provides a distributed robust optimal scheduling method, a device, equipment and a medium for a hydropower station, which are used for improving the generating capacity and the running stability of the hydropower station.

In a first aspect, the present invention provides a distributed robust optimal scheduling method for a hydropower station, including:

determining a warehouse-in flow error fuzzy set according to the historical warehouse-in flow estimated value and the historical warehouse-in flow actual value of at least one hydropower station;

Constructing a hydropower station distribution robust optimization scheduling model according to the historical output, the historical water head and the historical power generation flow of at least one hydropower station and the warehouse-in flow error fuzzy set;

reconstructing the hydropower station distribution robust optimization scheduling model based on a conditional risk value theory to obtain a reconstruction optimization scheduling model;

and determining the target output, the target water head and the target power generation flow of the target hydropower station according to the reconstruction optimization scheduling model and the warehouse-in flow estimated value of the target hydropower station.

In a second aspect, the present invention also provides a distributed robust optimal scheduling device for a hydropower station, including:

the fuzzy set determining module is used for determining a warehouse-in flow error fuzzy set according to the historical warehouse-in flow estimated value and the historical warehouse-in flow actual value of at least one hydropower station;

the model construction module is used for constructing a hydropower station distribution robust optimization scheduling model according to the historical output, the historical water head and the historical power generation flow of at least one hydropower station and the warehouse-in flow error fuzzy set;

the reconstruction model determining module is used for reconstructing the hydropower station distribution robust optimization scheduling model based on the conditional risk value theory to obtain a reconstruction optimization scheduling model;

In a third aspect, an embodiment of the present invention further provides an electronic device, including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the method comprises the steps of

The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the distributed robust optimal scheduling method of the hydropower station provided by any embodiment of the invention.

In a fourth aspect, the embodiment of the present invention further provides a computer readable storage medium, where the computer readable storage medium stores computer instructions, where the computer instructions are configured to cause a processor to implement the distributed robust optimization scheduling method for a hydropower station according to any embodiment of the present invention when executed.

According to the embodiment of the invention, a warehouse-in flow error fuzzy set is determined according to a historical warehouse-in flow estimated value and a historical warehouse-in flow actual value of at least one hydropower station; constructing a hydropower station distribution robust optimization scheduling model according to the historical output, the historical water head and the historical power generation flow of at least one hydropower station and the warehouse-in flow error fuzzy set; reconstructing the hydropower station distribution robust optimization scheduling model based on the conditional risk value theory to obtain a reconstruction optimization scheduling model; and determining the target output, the target water head and the target power generation flow of each hydropower station according to the reconstruction optimization scheduling model and the target flow estimated value of at least one hydropower station. According to the technical scheme, the storage flow error fuzzy set is determined according to the historical storage flow, the distributed robust optimal scheduling model is established, the waste water flow of the hydropower station is reduced, and the generating capacity and the running stability of the hydropower station are improved.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flowchart of a distributed robust optimal scheduling method for a hydropower station according to a first embodiment of the invention;

fig. 2 is a flowchart of a distributed robust optimal scheduling method for a hydropower station according to a second embodiment of the present invention;

fig. 3 is a flowchart of a distributed robust optimal scheduling method for a hydropower station according to a third embodiment of the present invention;

fig. 4 is a flowchart of a distributed robust optimal scheduling method for a hydropower station according to a fourth embodiment of the invention;

fig. 5 is a block diagram of a distributed robust optimal scheduling device for a hydropower station according to a fifth embodiment of the present invention;

Fig. 6 is a schematic structural diagram of an electronic device of a distributed robust optimal scheduling apparatus for a hydropower station according to a sixth embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

Fig. 1 is a flowchart of a distributed robust optimal scheduling method for a hydropower station according to a first embodiment of the present invention, where the present embodiment may be applicable to a situation of optimal scheduling for a hydropower station, and the method may be performed by a distributed robust optimal scheduling device for a hydropower station, where the distributed robust optimal scheduling device for a hydropower station may be implemented in a hardware and/or software form and specifically configured in an electronic device, for example, in a server.

As shown in fig. 1, the method includes:

s101, determining a warehouse-in flow error fuzzy set according to a historical warehouse-in flow estimated value and a historical warehouse-in flow actual value of at least one hydropower station.

In this embodiment, the historical warehouse-in flow estimated value may be an estimated value of warehouse-in flow in each scheduling period in the hydropower station history; the actual value of the historical warehouse-in flow can be the actual value of the warehouse-in flow in each scheduling time period in each scheduling period of the hydropower station historically; the binned flow error fuzzy set may be a fuzzy set of probability distribution of binned flow errors; wherein, one scheduling period may include a preset number of scheduling time periods; the binning flow error may be the difference between a historical binning flow estimate and a corresponding historical binning flow actual value.

It should be noted that, the scheduled time period and the preset number of scheduled time periods in one scheduling period may be set by a technician according to actual needs or practical experience, and the present invention is not limited. For example, one scheduling period may be 1 day, and the corresponding preset number may be 24, and one scheduling period is 1 hour.

S102, constructing a hydropower station distribution robust optimization scheduling model according to the historical output, the historical water head and the historical power generation flow of at least one hydropower station and the warehouse-in flow error fuzzy set.

In this embodiment, the historical output may be output in each scheduling period of the hydropower station historically; the historical water head can be the water head in each scheduling time period in each scheduling period of the hydropower station historically; the historical power generation flow can be the power generation flow in each scheduling time period in each scheduling period of the hydropower station historically; the hydropower station distribution robust optimization scheduling model can be a mathematical model for determining optimal scheduling parameters of the hydropower station; among other things, scheduling parameters include, but are not limited to, hydropower station output, hydropower station head, hydropower station power flow, and the like.

S103, reconstructing the hydropower station distribution robust optimization scheduling model based on the conditional risk value theory to obtain a reconstruction optimization scheduling model.

In this embodiment, the reconstruction optimization scheduling model may be a mathematical model that facilitates determination of optimal scheduling parameters for the hydropower station. Specifically, based on a conditional risk value theory, reconstructing the hydropower station distribution robust optimization scheduling model, and taking the reconstructed hydropower station distribution robust optimization scheduling model as a reconstruction optimization scheduling model.

S104, determining the target output, the target water head and the target power generation flow of each hydropower station according to the reconstruction optimization scheduling model and the target flow estimated value of at least one hydropower station.

In this embodiment, the target flow estimated value may include, but is not limited to, a warehouse-in flow estimated value of a first hydropower station and an incoming flow estimated value of other hydropower stations in each scheduling period of the hydropower station target scheduling period. The target scheduling period may be a next scheduling period of a current scheduling period of the hydropower station. The target output may be the output of each hydropower station in each scheduling period of the target scheduling period; the target water head can be the water head of each hydropower station in each scheduling time period of the target scheduling period; the target power flow rate may be a power flow rate of the target hydropower station in each scheduling period of the target scheduling period. The preset number can be set by a technician according to actual requirements or practical experience, and the invention is not limited to the preset number. For example, the scheduling period is 1 day, and the scheduling period is 1 hour, so that the output power and the power generation flow of the water head in each hour can be determined in 24 hours in the future according to the reconstruction optimization scheduling model and the estimated value of the warehouse-in flow of at least one hydropower station in 24 hours in the future.

In one specific embodiment, the product of the output force of each hydropower station in a scheduling time period and the output force conversion coefficient is taken as the generated energy of the corresponding hydropower station in the scheduling time period; the method comprises the steps that a determining function of the sum of the generated energy of all generated energy of each hydropower station in each scheduling time period is used as an objective function of a reconstruction optimization scheduling model; by way of example, the objective function may be determined by the following formula:

wherein f represents an objective function; t represents the number of scheduling time periods in the target scheduling period; t represents the sequence number of a scheduling time period of the hydropower station in one scheduling period; n represents the number of hydropower stations; i represents the serial number of the hydropower station; p (P) _i,t Indicating the output of the hydropower station i in a dispatching time period t; Δp represents the output power generation amount conversion coefficient.

Taking the output of each hydropower station in each scheduling time period of the target scheduling period when the objective function is the maximum as the target output; taking the water heads of all hydropower stations in all scheduling time periods of a target scheduling period when the objective function is the maximum as a target water head; and taking the power generation flow of each hydropower station in each scheduling time period of the target scheduling period when the objective function is the maximum value as the target power generation flow.

In one particular embodiment, the hydropower station may be at risk of out-of-limit, i.e., the hydropower station may be at risk of having a maximum storage capacity, a minimum storage capacity, or a reservoir level outside of a specified operating range. By adopting the distributed robust optimal scheduling method of the hydropower station, the hydropower station can keep a certain storage capacity so as to cope with uncertain incoming water flow and reduce the out-of-limit risk of the hydropower station.

According to the embodiment of the invention, a warehouse-in flow error fuzzy set is determined according to a historical warehouse-in flow estimated value and a historical warehouse-in flow actual value of at least one hydropower station; constructing a hydropower station distribution robust optimization scheduling model according to the historical output, the historical water head and the historical power generation flow of at least one hydropower station and the warehouse-in flow error fuzzy set; reconstructing the hydropower station distribution robust optimization scheduling model based on the conditional risk value theory to obtain a reconstruction optimization scheduling model; and determining the target output, the target water head and the target power generation flow of the target hydropower station according to the reconstruction optimization scheduling model and the warehouse-in flow estimated value of the target hydropower station. According to the technical scheme, the warehouse-in flow error fuzzy set is determined according to the historical warehouse-in flow, and the distributed robust optimal scheduling model is established, so that the waste water flow of the hydropower station is reduced, and the generating capacity and the running stability of the hydropower station are improved.

Example two

Fig. 2 is a flowchart of a distributed robust optimization scheduling method for a hydropower station according to a second embodiment of the present invention, where the embodiment of the present invention optimizes and improves the determination operation of the warehouse-in flow error fuzzy set based on the technical solution of the foregoing embodiment.

Further, the step of ' determining a fuzzy set of the warehouse-in flow errors according to the historical warehouse-in flow estimated value and the historical warehouse-in flow actual value of the at least one hydropower station ' is performed to be ' determining the total historical warehouse-in flow errors of the at least one hydropower station according to the historical warehouse-in flow estimated value and the historical warehouse-in flow actual value of the at least one hydropower station; determining an error distribution expected constraint condition, a variance distribution expected constraint condition, an error expected value constraint condition and an error variance constraint condition according to expected values and variances of total historical warehouse-in flow errors of at least one hydropower station; and determining a warehouse-in flow error fuzzy set according to the error distribution expected constraint condition, the variance distribution expected constraint condition, the error expected constraint condition and the error variance constraint condition so as to perfect the determination operation of the warehouse-in flow error fuzzy set.

In the embodiments of the present invention, the details are not described, and reference may be made to the description of the foregoing embodiments.

A method as shown in fig. 2, the method comprising:

s201, determining the total historical warehouse-in flow error of at least one hydropower station according to the historical warehouse-in flow estimated value and the historical warehouse-in flow actual value of the at least one hydropower station.

In this embodiment, the total historical warehouse-in flow error may include an error between the estimated historical warehouse-in flow value of each hydropower station and the actual value of the corresponding historical warehouse-in flow.

Specifically, for a hydropower station, determining a warehouse-in flow difference value between each historical warehouse-in flow estimated value and a corresponding historical warehouse-in flow actual value of the hydropower station, and taking each warehouse-in flow difference value of the hydropower station as a historical warehouse-in flow error of the hydropower station; and taking the historical flow errors of all the hydropower stations as the total historical warehouse-in flow errors.

S202, determining an error distribution expected constraint condition, a variance distribution expected constraint condition, an error expected constraint condition and an error variance constraint condition according to expected values and variances of total historical warehouse-in flow errors of at least one hydropower station.

In this embodiment, the error distribution expectation constraint condition may be used to constrain expectation when the warehouse-in flow error obeys the probability distribution; the variance distribution expectation constraint condition can be used for constraining expectation when variances of the warehouse-in flow errors obey probability distribution; the error expectation constraint condition is used for constraining the upper limit value and the lower limit value of the warehouse-in flow error; the error variance constraint condition is used for constraining the upper limit value and the lower limit value of the variance of the warehouse-in flow error.

Specifically, the expected value of the warehouse-in flow error when the warehouse-in flow error obeys the probability distribution is taken as the expected constraint condition of the error distribution. By way of example, the error distribution desired constraint may be determined by the following equation:

wherein ζ represents a warehouse-in flow error; μ represents an expected value of the warehouse entry flow error;

probability score representing warehouse-in flow error xiCloth; />

Representing obeying probability distribution +.>

Expected value at that time.

And determining the expected variance of the warehouse-in flow error as the expected constraint condition of the variance distribution when the variance of the warehouse-in flow error obeys the probability distribution. Illustratively, the variance distribution desired constraint may be determined by the following formula:

the probability distribution of the warehouse-in flow error xi is represented; />

Representing obeying probability distribution +.>

Expected value at that time; sigma (sigma) ² Indicating the variance of the binning flow error.

And determining the expected value of the warehouse-in flow error as an error expected constraint condition, wherein the expected value is not larger than an expected upper limit value and not smaller than an expected lower limit value. By way of example, the error expectation constraint may be determined by the following formula:

wherein μ represents an expected value of the warehouse entry flow error; μ represents a lower limit value of an expected value of the warehouse entry flow error;

An upper limit value indicating an expected value of the warehouse entry flow rate error.

And determining the variance of the warehouse-in flow error as an error variance constraint condition, wherein the variance is not larger than the variance upper limit value and not smaller than the variance lower limit value. By way of example, the error expectation constraint may be determined by the following formula:

wherein sigma ² Representing an expected value of the warehouse-in flow error; sigma (sigma) ² A lower limit value representing a variance of the warehouse-in flow error;

an upper limit value indicating the variance of the warehouse entry flow error.

The upper limit value and the lower limit value of the expected value of the warehouse-in flow error, and the upper limit value and the lower limit value of the variance of the warehouse-in flow error can be set independently by the technician according to the actual demand or practical experience, which is not limited by the present invention.

S203, determining a warehouse-in flow error fuzzy set according to the expected value probability constraint condition, the variance probability constraint condition, the expected value constraint condition and the variance constraint condition.

Specifically, the probability distribution of the warehouse-in flow errors meeting the expected value probability constraint condition, the variance probability constraint condition, the expected value constraint condition and the variance constraint condition is determined to be a warehouse-in flow error fuzzy set. Illustratively, the binned flow error fuzzy set can be expressed by the following formula:

Wherein,,

indicating a warehouse-in flow error fuzzy set; />

A probability distribution representing an uncertainty flow; />

A set of all probability distributions representing binned flow errors ζ; />

Representing obeying probability distribution +.>

Expected value at that time; μ is the estimated value error expected value of the warehouse-in flow; sigma (sigma) ² Variance of error of the estimated value of the warehouse-in flow; μ represents a lower limit value of an expected value of the warehouse entry flow error; />

An upper limit value indicating an expected value of the warehouse-in flow error; sigma (sigma) ² A lower limit value representing a variance of the warehouse-in flow error; />

An upper limit value indicating the variance of the warehouse entry flow error.

S204, constructing a hydropower station distribution robust optimization scheduling model according to the historical output, the historical water head and the historical power generation flow of at least one hydropower station and the warehouse-in flow error fuzzy set.

S205, reconstructing the hydropower station distribution robust optimization scheduling model based on the conditional risk value theory to obtain a reconstruction optimization scheduling model.

S206, determining the target output, the target water head and the target power generation flow of the target hydropower station according to the reconstruction optimization scheduling model and the target flow estimated value of the target hydropower station.

According to the embodiment of the invention, the total historical warehouse-in flow error of at least one hydropower station is determined according to the historical warehouse-in flow estimated value and the historical warehouse-in flow actual value of the at least one hydropower station; determining an error distribution expected constraint condition, a variance distribution expected constraint condition, an error expected value constraint condition and an error variance constraint condition according to expected values and variances of total historical warehouse-in flow errors of at least one hydropower station; and determining a warehouse-in flow error fuzzy set according to the warehouse-in flow expected value constraint condition, the variance distribution expected constraint condition, the error expected constraint condition and the error variance constraint condition. According to the technical scheme provided by the embodiment of the invention, the total historical warehouse-in flow error is determined, and the warehouse-in flow error fuzzy set is determined according to the expected value and the variance of the total historical warehouse-in flow error, so that the probability distribution of the warehouse-in flow error fuzzy set is more similar to that of the actual warehouse-in flow error, and the accuracy of the hydropower station distribution robust optimization scheduling model determined according to the warehouse-in flow error fuzzy set is further improved.

Example III

Fig. 3 is a flowchart of a distributed robust optimization scheduling method for a hydropower station according to a third embodiment of the present invention, where the determining operation of the distributed robust optimization scheduling model for the hydropower station is optimized and improved based on the technical solution of the foregoing embodiment.

Further, a hydropower station distribution robust optimization scheduling model is constructed according to the historical output, the historical water head and the historical power generation flow of at least one hydropower station and the warehouse-in flow error fuzzy set, and a hydropower station output function is determined according to the historical output, the historical water head and the historical power generation flow of at least one hydropower station; determining a robust opportunity constraint of the storage capacity distribution according to the storage flow error fuzzy set and the storage capacity constraint condition; and constructing a distributed robust optimal scheduling model according to the hydropower station output function and the stock capacity distributed robust opportunity constraint so as to perfect the determining operation of the distributed robust optimal scheduling model.

A method as shown in fig. 3, the method comprising:

s301, determining a warehouse-in flow error fuzzy set according to the historical warehouse-in flow estimated value and the historical warehouse-in flow actual value of at least one hydropower station.

S302, determining a hydropower station output function according to the historical output, the historical water head and the historical power generation flow of at least one hydropower station.

In this embodiment, the hydropower station output function may be used to determine the output of the corresponding hydropower station according to the head and the power flow of the hydropower station.

Optionally, determining the hydropower station output function according to the historical output, the historical water head and the historical power generation flow of the at least one hydropower station includes: and fitting the historical output according to the historical water head and the historical power generation flow of at least one hydropower station, and taking a fitting result function as a hydropower station output function.

In this embodiment, the fitting result function is a result function obtained by fitting the historical output.

Specifically, according to the historical water head and the historical power generation flow of at least one hydropower station, curve fitting is carried out on the corresponding historical output to obtain a fitting result function taking the historical output as a dependent variable and taking the historical water head and the historical power generation flow as independent variables, and the fitting result function is used as the hydropower station output function. The kind of fitting in the present invention is not limited, and may be, for example, linear fitting, quadratic fitting, or the like.

By way of example, according to the historical water head and the historical power generation flow of at least one hydropower station, the historical output is subjected to secondary fitting, so that a fitting result function is obtained and is used as the output function of the hydropower station, and the output function of the hydropower station can be represented by the following formula:

wherein P is _i,t The output of the hydropower station i in a dispatching time period t; h is a _i,t A water head of the hydropower station i in a dispatching time period t; q _i,t Generating flow for the hydropower station i in a scheduling time period t; c _0,i 、c _1,i 、c _2,i 、c _3,i 、c _4,i And c _5,i For fitting water determined in operationThe coefficients of the power station i.

It can be appreciated that by adopting the technical scheme, the historical output is fitted according to the historical water head and the historical power generation flow of at least one hydropower station to obtain the hydropower station output function, so that the hydropower station output function can fully describe the functional relationship among the water head, the power generation flow and the output, and the operation characteristics of a hydropower station unit are met, and the accuracy of hydropower station output determined according to the hydropower station output function is improved.

S303, determining the robust opportunity constraint of the storage capacity distribution according to the storage flow error fuzzy set and the storage capacity constraint condition.

In this embodiment, the reservoir capacity constraint condition may be used to constrain the reservoir capacity of the hydropower station; kuru distribution robustness opportunity constraints can be used to constrain the probability that a reservoir capacity constraint is established to constrain the reservoir slave of a hydropower station reservoir.

Specifically, the storage capacity is not larger than the upper limit value of the storage capacity of the corresponding hydropower station and not smaller than the lower limit value of the storage capacity of the corresponding hydropower station, and the storage capacity constraint condition is determined. Illustratively, the reservoir capacity constraint may be determined by the following formula:

wherein r is _i,t The reservoir capacity of the hydropower station i in the dispatching time period t is represented; r is (r) _i Representing the upper limit value of the storage capacity of the hydropower station i;

representing the lower limit of the storage capacity of hydropower station i.

Under the condition that the probability distribution of the warehouse-in flow errors belongs to a warehouse-in flow error fuzzy set, the infinitesimal bound for establishing the constraint condition of the warehouse capacity is not smaller than the corresponding confidence level, and the infinitesimal bound is used as the robust opportunity constraint of the warehouse capacity distribution; wherein the confidence level is the difference between 1 and the risk factor. Illustratively, the storage capacity distribution robust opportunity constraint may be determined by the following formula:

representing the lower limit of the storage capacity of hydropower station i. Epsilon _i Representing a risk factor; />

The probability distribution of the warehouse-in flow error is represented; />

Indicating a warehouse-in flow error fuzzy set; pr represents the probability that the reservoir capacity constraint condition is satisfied; inf represents the infinit.

It should be noted that, the upper limit value of the storage capacity, the lower limit value of the storage capacity and the risk coefficient may be set independently by a technician according to actual needs or practical experience, which is not limited in the present invention.

S304, constructing a distribution robust optimization scheduling model according to the hydropower station output function and the stock capacity distribution robust opportunity constraint.

Specifically, a mathematical model is constructed according to the hydropower station output function and the stock capacity distribution robust opportunity constraint and is used as a distribution robust optimization scheduling model.

S305, reconstructing the hydropower station distribution robust optimization scheduling model based on the conditional risk value theory to obtain a reconstruction optimization scheduling model.

S306, determining the target output, the target water head and the target power generation flow of the target hydropower station according to the reconstruction optimization scheduling model and the target flow estimated value of the target hydropower station.

According to the embodiment of the invention, the output function of the hydropower station is determined according to the historical output, the historical water head and the historical power generation flow of at least one hydropower station; determining a robust opportunity constraint of the storage capacity distribution according to the storage flow error fuzzy set and the storage capacity constraint condition; and constructing a distributed robust optimization scheduling model according to the hydropower station output function and the stock capacity distributed robust opportunity constraint. According to the technical scheme provided by the embodiment of the invention, the distributed robust optimal scheduling model with more flexible scheduling is constructed by determining the hydropower station processing function and the stock capacity distributed robust opportunity constraint, so that the hydropower station is optimally scheduled according to the reconstruction model of the distributed robust optimal scheduling model, the waste water flow of the hydropower station is reduced, and the generating capacity and the running stability of the hydropower station are improved.

Example IV

Fig. 4 is a flowchart of a distributed robust optimization scheduling method for a hydropower station according to a fourth embodiment of the present invention, where the determining operation of the reconstruction optimization scheduling model is optimized and improved based on the technical solution of the foregoing embodiment.

Further, reconstructing the distributed robust optimization scheduling model based on the conditional risk value theory to obtain a reconstructed optimal scheduling model which is thinned to be based on the conditional risk value theory, and reconstructing the reservoir capacity distribution robust opportunity constraint to be a reservoir capacity second order cone planning constraint; and replacing the stock capacity distribution robustness opportunity constraint in the distribution robust optimal scheduling model with the stock capacity second order cone planning constraint to obtain a reconstruction optimal scheduling model so as to perfect the determination operation of the reconstruction optimal scheduling model.

The method as shown in fig. 4, the method comprising:

s401, determining a warehouse-in flow error fuzzy set according to the historical warehouse-in flow estimated value and the historical warehouse-in flow actual value of at least one hydropower station.

S402, constructing a hydropower station distribution robust optimization scheduling model according to the historical output, the historical water head and the historical power generation flow of at least one hydropower station and the warehouse-in flow error fuzzy set.

Optionally, the hydropower station distribution robust optimization scheduling model comprises at least one of the following: flow constraint, hydropower station output constraint, water balance constraint and initial and final reservoir capacity constraint corresponding to the water balance constraint, or a water head determining function, a water head constraint corresponding to the water head function, an upstream water level determining function and a downstream water level determining function.

In this embodiment, the flow constraint may include a reject flow constraint and a power generation flow constraint, which are used to constrain a reject flow and a power generation flow of the hydropower station. Taking the existence of the reject flow as the reject flow constraint; and taking the power generation flow rate not larger than the power generation flow rate upper limit value and not smaller than the power generation flow rate lower limit value of the corresponding hydropower station as a power generation flow rate constraint condition. By way of example, the flow constraint may be determined by the following equation:

wherein s is _i,t Representing the waste water flow of the hydropower station i in a scheduling time period t; q _i,t Representing the power generation flow of the hydropower station i in the scheduling time period t; q _i Representing the upper limit value of the power generation flow of the hydropower station i;

the lower limit value of the power generation flow rate of the hydropower station i is shown.

Hydropower station output constraints are used to constrain the output of a hydropower station. And taking the output of the hydropower station as the output constraint, wherein the output of the hydropower station is not larger than the output upper limit value of the corresponding hydropower station and not smaller than the output lower limit value of the corresponding hydropower station. By way of example, hydropower station output constraints may be determined by the following equation:

Wherein P is _i,t Indicating the output of the hydropower station i in a dispatching time period t; p (P) _i Representing the lower limit value of the output of the hydropower station i;

the lower limit value of the output of the hydropower station i is shown.

The water balance constraint is used for incoming water flow, power generation flow and abandoned water flow in adjacent scheduling time periods of the hydropower station. For a scheduling time period, determining a first flow difference between the incoming water flow and the power generation flow in the scheduling time period; determining a second flow difference between the first flow difference value and the reject flow in the scheduling period; determining a first flow product of the second flow difference value and the flow volume conversion coefficient; determining the sum of the generated flow of the adjacent hydropower station and the flow of the abandoned water flow before the water flow delay in the scheduling time period; determining a second flow product of the flow sum and the flow volume conversion coefficient; accumulating the second flow products of all the upstream hydropower stations of the hydropower station to obtain a product addition result; and taking the sum of the storage capacity of the scheduling time period, the first flow product and the product sum result as the water balance constraint, wherein the sum is equal to the storage capacity of the next scheduling period of the hydropower station. Illustratively, the water balance constraint can be expressed by the following formula:

wherein r is _i,t Representing the storage capacity of the hydropower station i in the scheduling time period t; w (w) _i,t Representing an estimated value of the flow rate of the water coming from the hydropower station i in a scheduling time period t; q _i,t Representing the power generation flow of the hydropower station i in the period t; s is(s) _i,t Representing the waste water flow of the hydropower station i in a scheduling time period t; Δt represents a flow volume conversion coefficient; h _i An upstream power station set for hydropower station i; τ is the water flow delay of the adjacent hydropower station;

representing the power generation flow of the adjacent hydropower station j of the hydropower station i before the water delay tau of the scheduling time period t;

representing the reject flow of the adjacent hydropower station j of hydropower station i before the water casting time tau of the scheduling period t.

The initial and final storage capacity constraint is used for constraining the initial value and the final value of the storage capacity of the hydropower station in one scheduling period. And taking the initial value of the storage capacity of the hydropower station in the first scheduling period in one scheduling period as a preset initial value of the storage capacity, and taking the final value of the storage capacity of the hydropower station in the last scheduling period in one scheduling period as a preset final value of the storage capacity as a primary final storage capacity constraint. Wherein, the preset initial value and the preset final value of the storage capacity can be set independently by the technical personnel according to actual demands or practical experience, and the invention is not limited to the above. Illustratively, the initial and final reservoir capacity constraints may be determined by the following formula:

r _i,0 ＝r ⁰ _i,0 ,r _i,T ＝r ⁰ _i,T ；

Wherein r is _i,0 Representing the initial value of the reservoir capacity of the hydropower station i in a scheduling period; r is (r) ⁰ _i,0 Representing a preset initial value of the storage capacity; r is (r) _i,T Representing the end value of the reservoir capacity of the hydropower station i in a scheduling period; r is (r) ⁰ _i,T Indicating a preset end-of-reservoir value.

The head determination function is used to determine the head of the hydropower station. And determining a function of the water head of the hydropower station in a certain scheduling time period according to the difference value between the upstream water level and the downstream water level of the hydropower station in the scheduling time period as a water head determining function. Illustratively, the head determination function may be represented by the following formula:

h _i,t ＝z ^u _i,t -z ^d _i,t ；

wherein h is _i,t Representing the head of hydropower station i in a scheduled time period t; z ^u _i,t Representing the upstream water level of hydropower station i in a scheduling period t; z ^d _i,t Representing the water level of hydropower station i downstream of the scheduling period t.

The head restraint may be used to restrain the head of a hydropower station. And taking the water head not larger than the upper water head limit value of the corresponding hydropower station and not smaller than the lower water head limit value of the corresponding hydropower station as water head constraint. The upper limit value and the lower limit value of the water head can be set independently by a technician according to actual demands or practical experience, and the invention is not limited to the above. Illustratively, the head constraint may be determined by the following formula:

wherein h is _i,t Representing the head of hydropower station i in a scheduled time period t; h is a _i Representing the lower limit value of the water head of the hydropower station i;

the lower limit of the head of the hydropower station i is indicated.

The upstream water level determination function may be used to determine the upstream water level of the hydropower station based on the storage capacity of the hydropower station during the scheduled time period. And performing curve fitting on the upstream water level according to the reservoir capacity and the upstream water level of the historical scheduling period of the hydropower station to obtain an upstream water level determining function. By way of example, the upstream water level determination function may be expressed by the following formula:

wherein,,

representing the upstream water level of hydropower station i in a scheduling period t; r is (r) _i,t The reservoir capacity of hydropower station i in the scheduling period t is represented.

The downstream water level determination function may be used to determine the downstream water level of the hydroelectric power plant based on the power generation flow and the reject flow of the hydroelectric power plant during the scheduled time period. And performing curve fitting on the downstream water level according to the power generation flow, the waste water flow and the downstream water level of the historical scheduling time period of the hydropower station by using a curve fitting method to obtain a downstream water level determining function. By way of example, the upstream water level determination function may be expressed by the following formula:

wherein,,

representing the upstream water level of hydropower station i in a scheduling period t; q _i,t Representing the reservoir power generation flow of the hydropower station i in the scheduling time period t; s is(s) _i,t Representing the reject flow of hydropower station i in the scheduled time period t.

It can be appreciated that by adopting the above technical scheme, the hydropower station distribution robust optimization scheduling model can be constrained according to at least one of the flow constraint, the hydropower station output constraint, the water balance constraint and the initial and final reservoir capacity constraint corresponding to the water balance constraint, or the water head determining function and the water head constraint corresponding to the water head function, the upstream water level determining function and the downstream water level determining function, so that the accuracy of the target output, the target water head and the target flow determined according to the reconstruction optimization scheduling model is improved.

S403, reconstructing the probability constraint of the reservoir capacity distribution robustness into a reservoir capacity second order cone planning constraint based on the conditional risk value theory.

Specifically, for the storage capacity constraint condition in the storage capacity distribution robustness opportunity constraint, the storage capacity upper limit constraint condition that the storage capacity is not larger than the storage capacity upper limit value in the storage capacity constraint condition can be adjusted to be in an inequality constraint form of an uncertainty variable. Illustratively, the adjusted upper limit constraint on the reservoir capacity may be expressed by the following formula:

wherein,,

representing natural running water flow estimated values; / >

Representing decision variable functions, ++>

Representing an uncertainty coefficient of variation function.

And for the constraint condition of the lower limit of the storage capacity, the constraint condition of the lower limit of the storage capacity is not smaller than the constraint condition of the lower limit of the storage capacity, the constraint form of the lower limit of the storage capacity after being adjusted to the inequality constraint form of the uncertainty variable is similar to the constraint condition of the upper limit of the storage capacity, and the description is omitted.

And determining the adjusted stock capacity distribution robust opportunity constraint based on the adjusted stock capacity upper limit constraint condition and the stock capacity lower limit constraint condition. Illustratively, the adjusted reservoir capacity distribution robust opportunity constraint may be expressed by the following formula:

based on a conditional value risk theory, reconstructing the stock capacity distribution robust opportunity constraint based on the stock-in flow error fuzzy set into a stock capacity second order cone planning constraint. Illustratively, the reservoir capacity second order cone programming constraint may be expressed by the following formula:

wherein, gamma ₁ 、γ ₂ V, z, θ, β, and κ are auxiliary variables.

S404, replacing the reservoir capacity distribution robustness opportunity constraint in the distribution robustness optimization scheduling model with a reservoir capacity second order cone planning constraint to obtain a reconstruction optimization scheduling model.

Specifically, the stock capacity distribution robust opportunity constraint in the distribution robust optimal scheduling model is replaced by the stock capacity second order cone planning constraint, and the replaced distribution robust optimal scheduling model is used as a reconstruction optimal scheduling model.

S405, determining target output, target water head and target power generation flow of the target hydropower station according to the reconstruction optimization scheduling model and the target flow estimated value of the target hydropower station.

According to the embodiment of the invention, the constraint reconstruction of the probability of the robustness of the reservoir capacity distribution is based on the condition risk value theory, so that the reservoir capacity second-order cone planning constraint is realized; and replacing the stock capacity distribution robustness opportunity constraint in the distribution robust optimal scheduling model with the stock capacity second order cone planning constraint to obtain a reconstruction optimal scheduling model. According to the technical scheme, the reconstruction optimization scheduling model is obtained by reconstructing the stock capacity distribution robust opportunity constraint into the stock capacity second order cone planning constraint, so that the easiness of determining the target output, the target water head and the target power generation flow of each hydropower station according to the reconstruction optimization scheduling model is improved.

Example five

Fig. 5 is a block diagram of a distributed robust optimal scheduling device for a hydropower station according to a fifth embodiment of the present invention, where the present embodiment may be applicable to a situation of optimal scheduling of a hydropower station, and the distributed robust optimal scheduling device for a hydropower station may be implemented in a form of hardware and/or software and specifically configured in an electronic device, for example, a server.

The distributed robust optimal scheduling device of the hydropower station as shown in fig. 5 comprises a fuzzy set determining module 501, a model constructing module 502, a reconstruction model determining module 503 and a target output determining module 504. Wherein,,

the fuzzy set determining module 501 is configured to determine a fuzzy set of the warehouse-in flow error according to the estimated value of the historical warehouse-in flow of the at least one hydropower station and the actual value of the historical warehouse-in flow;

the model construction module 502 is configured to construct a hydropower station distribution robust optimization scheduling model according to the historical output, the historical water head, the historical power generation flow and the warehouse-in flow error fuzzy set of at least one hydropower station;

the reconstruction model determining module 503 is configured to reconstruct the hydropower station distribution robust optimization scheduling model based on the conditional risk value theory, so as to obtain a reconstruction optimization scheduling model;

the target output determining module 504 is configured to determine a target output, a target water head and a target power generation flow of each hydropower station according to the reconstructed optimal scheduling model and the target flow estimated value of at least one hydropower station.

The embodiment of the invention is used for determining a warehouse-in flow error fuzzy set according to the historical warehouse-in flow estimated value and the historical warehouse-in flow actual value of at least one hydropower station through a fuzzy set determining module; the model building module is used for building a hydropower station distribution robust optimization scheduling model according to the historical output, the historical water head and the historical power generation flow of at least one hydropower station and the warehouse-in flow error fuzzy set; the reconstruction model determining module is used for reconstructing the hydropower station distribution robust optimization scheduling model based on the conditional risk value theory to obtain a reconstruction optimization scheduling model; and the target output determining module is used for determining the target output, the target water head and the target power generation flow of each hydropower station according to the reconstruction optimization scheduling model and the target flow estimated value of at least one hydropower station. According to the technical scheme, the warehouse-in flow error fuzzy set is determined according to the historical warehouse-in flow, and the distributed robust optimal scheduling model is established, so that the waste water flow of the hydropower station is reduced, and the generating capacity and the running stability of the hydropower station are improved.

Optionally, the fuzzy set determination module 501 includes:

the flow error determining unit is used for determining the total historical warehouse-in flow error of the at least one hydropower station according to the historical warehouse-in flow estimated value and the historical warehouse-in flow actual value of the at least one hydropower station;

the flow error determining unit is used for determining an error distribution expected constraint condition, a variance distribution expected constraint condition, an error expected value constraint condition and an error variance constraint condition according to expected values and variances of total historical warehouse-in flow errors of at least one hydropower station;

and the flow error determining unit is used for determining a warehouse-in flow error fuzzy set according to the error distribution expected constraint condition, the variance distribution expected constraint condition, the error expected value constraint condition and the error variance constraint condition.

Optionally, the model building module 502 includes:

the power output function determining unit is used for determining a power output function of the hydropower station according to the historical power output, the historical water head and the historical power generation flow of at least one hydropower station;

the opportunity constraint determining unit is used for determining the robust opportunity constraint of the storage capacity distribution according to the storage flow error fuzzy set and the storage capacity constraint condition;

and the scheduling model construction unit is used for constructing the distributed robust optimization scheduling model according to the hydropower station output function and the stock capacity distributed robust opportunity constraint.

Optionally, the output function determining unit includes:

and the output function determining subunit is used for fitting the historical output according to the historical water head and the historical power generation flow of at least one hydropower station, and taking the fitting result function as the hydropower station output function.

Optionally, the reconstruction model determining module 503 includes:

the reservoir capacity constraint reconstruction unit is used for reconstructing the reservoir capacity distribution robust opportunity constraint into a reservoir capacity second order cone planning constraint based on the conditional risk value theory;

and the reconstruction model determining unit is used for replacing the stock capacity distribution robust opportunity constraint in the distribution robust optimization scheduling model with the stock capacity second order cone planning constraint to obtain the reconstruction optimization scheduling model.

Optionally, the distributed robust optimal scheduling device of the hydropower station, wherein the distributed robust opportunity constraint condition comprises at least one of the following:

flow constraint, hydropower station output constraint, water balance constraint and initial and final reservoir capacity constraint corresponding to the water balance constraint, or a water head determining function, a water head constraint corresponding to the water head function, an upstream water level determining function and a downstream water level determining function.

The distributed robust optimal scheduling device for the hydropower station can execute the distributed robust optimal scheduling method for the hydropower station provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the distributed robust optimal scheduling method for each hydropower station.

Example six

Fig. 6 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 6, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as a distributed robust optimal scheduling method for a hydropower station.

In some embodiments, the distributed robust optimal scheduling method of a hydropower station may be implemented as a computer program, which is tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the distributed robust optimal scheduling method of a hydropower station described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the distributed robust optimized scheduling method of the hydropower station in any other suitable way (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims

1. A distributed robust optimal scheduling method for a hydropower station is characterized by comprising the following steps:

and determining the target output, the target water head and the target power generation flow of each hydropower station according to the reconstruction optimization scheduling model and the target flow estimated value of at least one hydropower station.

2. The method of claim 1, wherein determining the binned flow error fuzzy set based on the historical binned flow estimate and the historical binned flow actual value of the at least one hydropower station comprises:

determining a total historical warehouse-in flow error of at least one hydropower station according to the historical warehouse-in flow estimated value and the historical warehouse-in flow actual value of the at least one hydropower station;

determining an error distribution expected constraint condition, a variance distribution expected constraint condition, an error expected value constraint condition and an error variance constraint condition according to the expected value and variance of the total historical warehouse-in flow error of the at least one hydropower station;

and determining a warehouse-in flow error fuzzy set according to the error distribution expected constraint condition, the variance distribution expected constraint condition, the error expected value constraint condition and the error variance constraint condition.

3. The method of claim 1, wherein said constructing a hydropower station distribution robust optimization scheduling model from the historical output, the historical head and the historical power flow of the at least one hydropower station, and the binned flow error fuzzy set comprises:

determining a hydropower station output function according to the historical output, the historical water head and the historical power generation flow of at least one hydropower station;

determining a robust opportunity constraint of the storage capacity distribution according to the storage flow error fuzzy set and the storage capacity constraint condition;

and constructing the distributed robust optimization scheduling model according to the hydropower station output function and the stock capacity distributed robust opportunity constraint.

4. A method according to claim 3, wherein said determining a hydropower station output function from the historical output, the historical head and the historical power flow of the at least one hydropower station comprises:

and fitting the historical output according to the historical water head and the historical power generation flow of at least one hydropower station, and taking a fitting result function as a hydropower station output function.

5. The method according to claim 1, wherein the reconstructing the hydropower station distribution robust optimization scheduling model based on the conditional risk value theory to obtain a reconstructed optimization scheduling model includes:

Reconstructing the bin capacity distribution robust opportunity constraint into a bin capacity second order cone planning constraint based on a conditional risk value theory;

and replacing the stock capacity distribution robustness opportunity constraint in the distribution robust optimal scheduling model with the stock capacity second order cone planning constraint to obtain a reconstruction optimal scheduling model.

6. The method of claim 1, wherein the hydropower station distribution robust optimization scheduling model comprises at least one of the following:

7. A distributed robust optimal scheduling device for a hydropower station, comprising:

and the target output determining module is used for determining the target output, the target water head and the target power generation flow of each hydropower station according to the reconstruction optimization scheduling model and the target flow estimated value of at least one hydropower station.

8. The method of claim 7, wherein the fuzzy set determination module comprises:

9. An electronic device, the electronic device comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the distributed robust optimal scheduling method of a hydropower station according to any one of claims 1-6.

10. A computer readable storage medium, characterized in that it stores computer instructions for causing a processor to implement the distributed robust optimal scheduling method of a hydropower station according to any one of claims 1-6 when executed.